Сравнение методов

	ARFIMA: Модель дробно-интегрированного ARMA	Квантильная регрессия
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1980	1978
Автор метода	Granger & Joyeux (1980); Hosking (1981)	Koenker & Bassett
Тип	Long-memory time series model	Conditional quantile regression
Основополагающий источник	Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Другие названия	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	conditional quantile regression, regression quantiles, Kantil Regresyon
Связанные	5	5
Сводка	ARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.